Automatic Segmentation of the Heart and Aorta in Medical 3-D Scans Without Contrast Media Injections

ABSTRACT

A method automatically segments the heart and abdominal aorta from volumetric images without the need to inject iodine contrast media into the subject. The method automatically quantifies arterial plaque (hard plaque, soft plaque or both) in the cardiovascular system. Plaque definitions include subject specific in vivo blood/muscle density measurements, subject specific voxel statistical parameters and 2-D and 3-D voxel connectivity criteria, which are used to automatically identify the plaques. The locations and outlines of the major arteries are determined in a 3-D coordinate system and the specific coordinates of the detected plaques are displayed in a plaque map for follow-up exams or ease in plaque review and reporting the results.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.10/303,663, filed on Nov. 23, 2002, which claims priority to U.S.Provisional Patent Application No. 60/333,223, filed Nov. 24, 2001, bothof which are incorporated in their entireties by reference herein.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to the field of medical imaging usingcomputed tomography (“CT”), and in particular, to measurements ofcalcium in the vascular system of a living body.

2. Description of the Related Art

Cardiovascular disease, including heart attacks and strokes, is causedby atherosclerotic plaque build-up from calcification of the arteries ofthe body, including the coronary arteries, cerebral arteries, renalarteries, etc., and is the leading cause of death in the Western world.Coronary artery disease (“CAD”), the leading cause of death in theUnited States, is receiving a great amount of attention, particularlywith regard to the need for noninvasive, safe, and low-cost tests todiagnose arterial plaque.

Strong correlations have been found between coronary arterycalcification and coronary artery occlusions as detected at autopsy.Coronary artery calcium has been shown to be diagnostic ofatherosclerotic coronary artery disease. Studies have shown thatarterial calcium development is intimately associated with vascularinjury and atherosclerotic plaque development. Coronary calcium ispresent in most patients who suffer acute coronary events. Although manypatients with CAD exhibit clinical signs of CAD, including angina ornon-fatal myocardial infarction, about half of CAD patients have nosymptoms before their sudden deaths.

Advanced atherosclerosis is usually associated with plaquecalcification. More than 80% of coronary lesions are calcified, and thepresence of calcification is almost certainly associated with plaque.Conversely, the absence of coronary calcium is diagnostic of no coronarylesions with a confidence of 95% to 98%.

While early detection and prevention of atherosclerotic plaque incoronary arteries is desirable, coronary calcium screening is notavailable in most communities of the U.S. or in the remainder of theworld. Conventional noninvasive methods of detection, such as stresstests, are limited by poor performance.

Ultrafast electron beam computed tomography (“EBCT”) scanners have shownsuperior sensitivity for detection and quantification of cardiaccalcifications. These scanners allow rapid image acquisition times,which essentially freeze cardiac motion and allow noninvasivemeasurement of coronary calcifications. More recently, fast, spiralmultidetector computed tomography (“MDCT”) scanners have been developedwith subsecond scan times. Although not as fast as EBCT scanners, MDCTscanners still have scan speeds sufficient to essentially freeze cardiacmotion. These scanners are quickly being installed and are increasinglybeing used for coronary calcium measurements. These scanners generatetwo-dimensional axial CT images which can be stacked together to producea three-dimensional image of a volume of the body.

Conventional single-slice computed tomography (“CT”) scanners are widelyavailable, being present in almost all U.S. hospitals, even hospitals ofsmall size. These conventional CT scanners have image acquisition timesmuch too long to produce images which freeze cardiac motion, but theyare used extensively for imaging the remainder of the body. Currentcardiac CT images are acquired with ECG gating, which adds some,although manageable, complexity.

In vitro CT measurements of coronary calcium in cadaver hearts have beencompared to later ash weight measurements of calcium content. Estimatesof the calcium mass from the CT measurements correlated highly with theactual calcium mass of the ashed specimens (r=0.97). Although thecorrelation was high, the regression equation relating the actual massto the mass estimates indicated that the CT mass estimates consistentlyunderestimate actual coronary calcium mass.

SUMMARY OF THE INVENTION

Methods are disclosed which provide automatic location andquantification of arterial calcium in the major arteries of the body.The preferred embodiments disclose methods for coronary and aorticcalcium analysis using computerized tomography. The methods preferablyuse calibrated images for improved precision, but can operate withoutcalibration. The number of interactions by the operator with the imagesis greatly reduced and in some cases approach zero. The reproducibilityof measurements is improved since the operator's subjective judgment isessentially eliminated.

In one preferred embodiment, calcium in the coronary arteries of livingpatients can be quantified reproducibly by automatic methods. The heartis segmented using preferably calibrated images. The heart is locatedand its boundaries are identified in 3-D space. In specific patients, itmay be necessary for the operator to mark the inferior boundary of theheart to further aid the software in separating heart tissue from liveror sternum. The mode of a histogram analysis of the 3-D segmented heartprovides a best measure of the blood/heart tissue density. This value isused to smooth the heart image to remove streak artifacts. The histogrammode is also incorporated into the calibration equation. The aorta islocated automatically by using shape, size and density constraints. Theaorta is segmented off from the heart and is also used as a beginninglocation in the search for coronary artery locations.

The auto search algorithm uses criteria based on the location of fat,calcium and contour inflection points to automatically determinelocations of the four major coronary arteries (RM, LAD, CIRC, and RCA).The software determines the locations with 3-D coordinates which aredisplayed onto a 3-D surface rendered image of the heart. Each arterylocation is highlighted and identified. The operator may override thesoftware and input corrections on the locations of the arteries. Oncethe arteries are located, a 3-D search ROI is automatically placedaround the line locating the arteries without operator input. Thesoftware then searches each region within the 3-D ROIs encompassing eachvessel to identify plaque above a calibrated threshold value. Since theCT images are calibrated with a calcium equivalent calibration phantomand further corrected with an in vivo blood sample measure, thethreshold values are reproducible and independent of scanner type andpatient size and body composition.

The locations of calcified plaque above the calibrated threshold arerecorded along with their x, y, and z coordinates. Positivecalcifications are also automatically identified with the artery inwhich they are located. On repeat scans at a later date, comparison ofcalcium increase or decrease can be carried out because of thecalibration methods and known calcification locations.

The methods improve reproducibility by use of the calibration methodsand automatic location and quantification. Reproducibility is alsoimproved due to the use of machine defined objective and reproduciblecriteria. The automatic algorithm greatly speeds the exam as eachindividual CT slice and each individual calcification need not beidentified and analyzed.

Two additional methods to analyze coronary calcium are disclosed whichdo not require specific locations of the coronary arteries. In bothmethods, the boundaries of the heart are located and segmented fromsurrounding tissue. The heart volume with coronary arteries is analyzedas a 3-D volume with measured volume, shape and mass. A coordinatesystem is determined for the heart using the location of the center ofthe aorta as the superior coordinate. In one method the heart is dividedinto three regions from the inferior end to the superior end of theheart. The lower, inferior region is known to have calcium only in thecoronary arteries. The mid one third of the heart will usually containthe valves, which are sometimes calcified. It is desirable to excludethis calcium from the coronary calcium measure. A high calcium contentin the region is a flag for the operator to evaluate and potentiallyremove the contribution from calcified valves. A search ROI can beplaced around such regions and the calcium content subtracted from thetotal calcium measure. Alternatively, the calcium is located with x,y,zcoordinates referenced to the heart volume coordinates. An algorithmdefines the heart region for analysis as a shell surrounding the heartwall in 3-D space. Only calcium with a specified distance from the heartboundaries is included. The superior one-third of the heart is analyzednext. The aorta will be located in this region, and frequently containscalcium. It is desirable to exclude this calcium from the measure. Theoperator can evaluate high calcium content for this region and removeaortic calcium by manual ROI placement and subtraction. This method mayprovide high reproducibility and does not require specific coronaryartery locations to be determined. The locations of calcium are,however, not identified with specific arteries.

A modification of this method does not divide the segmented heart volumeinto three regions, but rather analyzes only the surface shell region ofthe heart. The coronary arteries are known to be located exterior to theheart muscle wall and in between heart wall tissue. The coronaryarteries can therefore be assumed to be located near the heartboundaries and outside the heart muscle. From the segmented heartvolume, the surface coordinates of the heart are defined. The search ROIis set on the surface shell with a defined thickness of several pixels.Calcifications exceeding the set threshold are quantified. Valve calciumand aortic calcium are excluded based on their locations. In some cases,the aortic calcium will be included and will require manual exclusion.

In a second preferred embodiment of the invention, calcium in the aortais quantified automatically and reproducibly. In this method, thecalibration phantom is preferably first located automatically by priorart methods. The vertebral bodies are next located automatically, alsoby prior art methods. The exterior cortical margins of the vertebralbodies are located automatically by segmentation methods. A search ROIis placed automatically anterior to the cortical margin, which, withhigh probability, will encompass the aorta. The images within the searchROIs are cropped and reformatted to sagittal and coronal views. Thereduced quantity of overlying tissue provides 2-D images with heightenedcontrast which can display the aorta and calcifications. The operatorprovides the superior and inferior limits of the aorta on the sagittalview or coronal view. A connecting line between these two points shouldintersect the aorta on all slices. If not, a third cursor click or moremay be required. The intersection of the line and the aorta regionprovides a seed point for the software. Region growing methods or othersimilar methods are used to locate the aorta boundaries automatically.The boundaries are then searched with calibrated threshold criteria toidentify calcifications in the aorta. The software determines thelocation of calcium in 3-D space. The results are displayed on thesagittal view, with exaggerated contrast if required to clearly showtheir presence and location. The calcium is summed and presented as acalcium measure which may or may not be normalized to the crosssectional area of the aorta or to the aorta volume.

In a second preferred method to automatically quantify calcium in theaorta, a different auto search algorithm is used. This algorithm hasadvantages in older patients who may have rotated vertebral bodies.

Either search algorithm will operate on any major artery of the body,including the carotids. Either algorithm can operate with or withoutcalibration. However, performance is improved with external calibrationusing a phantom and internal calibration using blood as a surrogateinternal calibration sample.

In accordance with certain embodiments described herein, a methodmeasures calcium in arteries of the human body using computed tomography(CT). The method acquires CT images containing voxels representative ofx-ray attenuation in the body. The method comprises calibrating the CTimages. The method further comprises identifying the location of atleast one artery automatically. The method further comprises identifyingcalcium within the artery automatically. The method further comprisesquantifying the calcium using the calibrated CT image.

In accordance with certain other embodiments described herein, anautomated method measures coronary calcium in a living subject usingx-ray computed tomography (CT). The method acquires at least one CTimage containing voxels representing x-ray attenuation of the subject.The method of analysis requires operator interactions and comprisesanalyzing the images in a computer to identify the boundaries of theheart. The method further comprises identifying the approximate locationof at least one coronary artery. The method further comprises placing aregion-of-interest (ROI) surrounding the artery location. The methodfurther comprises analyzing the ROI to identify voxels above a thresholdvalue. The method further comprises determining the calcium content.

In accordance with still other embodiments described herein, a methodmeasures coronary calcium in a living subject using x-ray computedtomography (CT). The method acquires at least one CT image containingvoxels representing x-ray attenuation of the subject. The method ofanalysis of the images comprises identifying the approximate location ofat least one coronary artery. The method further comprises placing aregion-of-interest (ROI) surrounding the artery location. The methodfurther comprises analyzing the ROI to identify voxels above a thresholdvalue. The method further comprises determining the calcium content.

In accordance with still other embodiments described herein, a systemquantifies calcium in coronary arteries of the body using x-ray computedtomography (CT). The system comprises means for acquiring images of thearteries. The images are calibrated with a calcium equivalent phantom.The system further comprises means for determining the approximatelocation of at least one coronary artery automatically. The systemfurther comprises means for automatically positioning a search ROI whichsurrounds the artery location on at least one image. The system furthercomprises means for evaluating image elements within said ROI to locatecalcium. The system further comprises means for quantifying the calciumin the artery to generate a total calcium measure.

In accordance with still other embodiments described herein, a methoddetermines calcium in arteries in a living body from at least onecomputed tomography image. The image comprises image elementsrepresentative of x-ray attenuation in said body. The method comprisesscanning a calcium equivalent calibration referenced phantomsimultaneously with the body. The method further comprises multiplyingsaid image elements by the phantom calibration equation. The methodfurther comprises automatically locating the calcium in the artery. Themethod further comprises determining the mass of calcium.

In accordance with still other embodiments described herein, a systemdetermines calcium in at least one artery of a living subject using atleast one x-ray computed tomography image. The system comprises meansfor locating a position of the artery on at least one image withoutoperator interaction. The system further comprises means for placingregions-of-interest (ROI) surrounding the position of artery withoutoperator interaction. The system further comprises means for locatingcalcium within the ROI without operator interaction. The system furthercomprises means for quantifying the calcium.

In accordance with still other embodiments described herein, a methodquantifies calcium in at least one artery of a human subject from atleast one computed tomography image. The method comprises scanning areference calibration phantom containing calcium simultaneously with thesubject. The method further comprises calibrating at least one imageelement using the calibration phantom. The method further compriseslocating image elements within an image region corresponding to blood orthe heart. The method further comprises determining a best measure ofthe density values of image elements within the image region. The methodfurther comprises determining a calibration equation using the slope ofHU values from the calibration phantom and intercept from said bestmeasure. The method further comprises correcting image elements withinthe image by the calibration equation. The method further comprisesidentifying image elements above a threshold value. The method furthercomprises determining calcium within the artery.

In accordance with still other embodiments described herein, a methodquantifies calcium in the aorta of a living subject from at least onecomputed tomography image. The method comprises locating a vertebralbody in at least one image. The method further comprises locating anexterior cortical bone margin of the vertebral body in the image. Themethod further comprises defining a plurality of search contours. Themethod further comprises identifying calcified regions along each searchcontour having density values above a predetermined threshold value. Themethod further comprises quantify calcium in the aorta.

In accordance with still other embodiments described herein, a methodmeasures calcification in the aorta of a living subject. The methodcomprises automatically defining a search region-of-interest (ROI)anterior to a vertebral body. The search ROI encompasses the aorta. Themethod further comprises locating boundaries of the aorta. The methodfurther comprises identifying calcified regions within the boundaries ofthe aorta having densities above a predetermined threshold value. Themethod further comprises measuring a calcium content.

In accordance with still other embodiments described herein, a methodautomatically identifies approximate locations of coronary arteries of aliving body within a plurality of images of a heart region of the body.Each image comprises image elements indicative of densities ofcorresponding body structures, and each image element has correspondingposition coordinates. The method comprises (a) identifying atwo-dimensional surface boundary of the heart in each image. The surfaceboundary is represented as a surface contour in the image. The methodfurther comprises (b) searching the surface contour of an image forinflection point image elements and recording the position coordinatesfor each inflection point image element. The method further comprises(c) searching the surface contour of the image for groups of fat densityimage elements and recording the position coordinates for each fatdensity image element. Each group of fat density image elementscomprises a group of neighboring image elements defining a volumegreater than or equal to a predetermined fat threshold volume. Each fatdensity image element has a density value within a predetermined valuerange. The method further comprises (d) searching the surface contour ofthe image for groups of calcium density image elements and recording theposition coordinates for each calcium density image element. Each groupof calcium density image elements comprises a group of neighboring imageelements defining a volume greater than or equal to a predeterminedcalcium threshold volume. Each calcium density image element has adensity value greater than or equal to a predetermined threshold value.The method further comprises repeating (b)-(d) for subsequent imagesuntil all the two-dimensional surface boundaries are completelysearched. The method further comprises (f) assigning a first weightingfactor to each inflection point image element, a second weighting factorto each fat density image element, and a third weighting factor to eachcalcium density image element. The method further comprises (g)calculating a set of lines. Each line provides an approximate locationof a coronary artery and corresponds to a weighted location average ofthe inflection point image elements, the fat density image elements, andthe calcium density image elements.

In accordance with still other embodiments described herein, a methodmeasures coronary artery calcification of a living body. The methodcomprises providing a plurality of noninvasive internal images of thebody. Each image comprises image elements indicative of densities ofcorresponding body structures. The method further comprises identifyingboundaries of the heart within the plurality of images, the boundariesdefining a heart volume. The method further comprises dividing the heartvolume into an inferior region, a middle region, and a superior region.The method further comprises locating calcium within the inferior regionand attributing the inferior region calcium to calcification of thecoronary arteries. The method further comprises locating calcium withinthe middle region and attributing a portion of the middle region calciumto calcification of the coronary arteries and a remaining portion of themiddle region calcium to calcification of the heart valves. The methodfurther comprises locating calcium within the superior region andattributing a portion of the superior region calcium to calcification ofthe coronary arteries and a remaining portion of the superior regioncalcium to calcification of the aorta.

In accordance with still other embodiments described herein, a methodmeasures coronary artery calcification of a living body. The methodcomprises providing a plurality of noninvasive internal images. Eachimage comprises image elements indicative of densities of correspondingbody structures. The method further comprises identifying boundaries ofthe heart within the plurality of images, the boundaries defining aheart volume. The method further comprises defining surface coordinatesof the heart volume. The method further comprises defining a shellvolume at the surface coordinates. The shell volume has a predeterminedthickness. The method further comprises identifying calcified regionswithin the shell volume having density values above a predeterminedthreshold value and attributing the calcified regions to calcificationof the coronary arteries.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow diagram of an exemplary embodiment of a method formeasuring calcification in at least a portion of the vascular system ofa living body.

FIG. 2 is a flow diagram of an exemplary embodiment of a method formeasuring calcification in at least one coronary artery of a livingbody.

FIG. 3 is a flow diagram of one embodiment of a method for automaticallyidentifying an approximate location of at least one coronary artery of aliving body.

FIG. 4 is a flow diagram of one embodiment for segmenting out portionswhich do not correspond to the heart from an x-ray CT image.

FIG. 5 illustrates an exemplary histogram of a CT image.

FIG. 6A schematically illustrates a surface rendered three-dimensionalimage of the heart showing the location of the right coronary artery,the left anterior descending artery, and the main coronary artery.

FIG. 6B schematically illustrates sagittal and coronal reformations ofthe data illustrated in FIG. 6A.

FIGS. 7A, 7B, and 7C illustrate a flow diagram of one embodiment of amethod for measuring coronary artery calcification of a living body.

FIG. 8 is a flow diagram of another embodiment of a method for measuringcoronary artery calcification of a living body.

FIGS. 9A and 9B illustrate a flow diagram of another embodiment of amethod of measuring coronary artery calcification of a living body.

FIG. 10 illustrates a depiction of a cross section through the abdomenshowing a calibration phantom, a vertebra, and the aorta withcalcification.

FIG. 11 illustrates a depiction of the procedure used in certainembodiments to automatically locate the aorta.

FIG. 12 illustrates a depiction of the scan procedure used in certainembodiments to automatically locate the aorta and aortic calcium.

FIGS. 13A and 13B illustrate a flow diagram of another embodiment for amethod for automatically locating and quantifying aortic calcium.

FIG. 14 illustrates a depiction of a sagittal view of a region of theaorta reconstructed from cropped images.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

A premise of certain embodiments described herein is that theatherosclerotic process is systemic, affecting all of the arteries ofthe body in a similar way. Although plaque build-up may occur and/orprogress at different rates in different arteries, this process occursthroughout the body. Others have studied the close relationship ofcardiac calcium and extracoronary plaque by comparing ultrasoundmeasurements of carotid, aortic, and femoral plaque with ultrafast CTmeasurements of coronary calcifications. These studies have shown thatpatients with coronary calcifications had a higher prevalence of aorticand femoral plaque.

The abdominal aorta is a site of atherosclerotic plaque containingcalcifications. The aorta has minimal motion, thus allowing easy imagingusing conventional CT scanners. Although aortic calcifications have beenassociated subjectively with atherosclerotic disease, aortic calcium hasnot been quantified or proposed as a quantitative diagnostic test forgeneralized cardiovascular risk. Measurements of coronary and aorticcalcium in 200 patients using a fast CT scanner with phantom calibrationhave shown a strong correlation between aortic and cardiaccalcifications.

Although CT images are inherently quantitative, the recorded attenuationvalues, expressed in Hounsfield units (“HU”), can vary significantly dueto a variety of technical factors. These factors include, but are notlimited to, patient size and composition, and scanner-dependent factorssuch as beam hardening and scattered x-ray photons. The effective x-raybeam energy and the beam hardening error can vary with scanner design,x-ray tube and filtration, software functions and corrections, geometry,and body composition. Significantly different attenuation values for thesame subject may be recorded on different CT machines. Even when thesame CT machine is used, the attenuation values may vary at differenttimes due to x-ray tube aging and/or electronic drift.

Calibration phantoms can provide a method to precisely quantify theattenuation by overcoming technical variations in CT scanners andphysical differences from patient to patient. For example, QCT bonedensitometry measurements of the lumbar spine have been improved byscanning phantoms simultaneously with the patient to calibrate the imageto a known standard. Exemplary calibration processes are described byArnold in U.S. Pat. No. 4,922,915, and in U.S. Pat. No. 6,990,222, bothof which are incorporated in their entirety by reference herein.

Quantitative CT measurements are typically facilitated by manualplacement of a region-of-interest (“ROI”) within specific areas of theCT image to be measured in HU units. The ROI is usually shown on a videoscreen as a bright line outline which has known (x,y) coordinates in theimage voxel matrix. The ROI is adjustable for size, shape, or size andshape, and is positioned by the operator in the target area ofindividual CT slices by manually moving the ROI under cursor controlusing a keyboard, a light pen, or a mouse. Such manual procedures arelaborious and time consuming, as well as being prone to error in exactpositioning. In addition, many objects in the image, such as calcifiedplaque, have an irregular margin such that a fixed geometry ROI willtypically be overinclusive by containing some non-calcified surroundingtissue or will be underinclusive by omitting a portion of the calcifiedplaque. Such errors can be quite large depending on the detail size.

Prior art software systems for coronary calcium measurements utilize asearch ROI manually placed by the operator to aid the software inlocating the target region. Typically, the search ROI is much largerthan the target detail and is manually placed to fully surround thetarget region. The software then uses thresholding to aid in identifyingthe calcifications. For example, voxels anywhere in the image with HUvalues greater than a predetermined value are colored or highlighted.The operator then places the search ROI around the highlightedcalcifications by using a mouse, a cursor, or a pointer to manually movethe search ROI. Even though this manual procedure significantly aids thesoftware in locating and analyzing the calcifications, the operator isrequired to manually place the search ROI or pointer on all CT imagesand on all calcified regions within each image. Such exemplary prior artsoftware systems are described by Arnold in U.S. Pat. No. 4,922,915;Judd E. Reed et al. in System for Quantitative Analysis of CoronaryCalcification via Electron Beam Computed Tomography, SPIE Proceedings,Vol. 2168, Medical Imaging 1994, Physiology and Function fromMultidimensional Images, Eric A. Hoffman and Raj S. Acharya, eds., pp.43-53; A. S. Agatston et al. in Quantification of Coronary ArteryCalcium Using Ultrafast Computed Tomography, J. Am. Coll. Cardiol., Vol.15, 1990, pp. 827-832; R. Detrano et al., in Accurate Coronary CalciumPhosphate Mass Measurements from Electron Beam Computed Tomograms, Am.J. Card. Imaging, Vol. 9, No. 3, July 1995, pp. 167-173; and Scimage,Inc. of Los Altos, Calif., in Calcified Plaque Analysis (CPA),commercial brochure, 2001, all of which are incorporated in theirentireties by reference herein.

However, manually placing the search ROI limits the usefulness of CTimaging for calcium measurements. To cover the whole heart for coronarycalcium measurements, several CT image slices are required. The operatormust display and analyze each CT image slice and manually place one ormore search ROIs in each image corresponding to the calcified regions inthe image. The operator must use judgment in placing the search ROIs,which can lead to errors and loss of reproducibility on follow-up scans,thus degrading the ability to monitor changes in calcification.Similarly, manual placement of the search ROIs for aortic calciummeasurements, requiring on the order of forty or more CT image slices,would be very laborious and impractical in a busy CT clinic. Forquantification of coronary, aortic, or vascular calcifications anywherein the body, many CT image slices are required, and manual analysis isvery time consuming and subject to human error. It is thereforedesirable to have automatic software methods for analysis ofcalcification using CT images which are fast and reliable.

Certain embodiments described herein provide an automatic and accuratemethod of quantifying coronary or aortic calcification usingconventional single-slice CT scanners or MSCT scanners. Use ofembodiments described herein can provide a safe, easy, noninvasive testfor cardiovascular disease, which can be readily performed in mostcommunities by using lower-cost conventional CT scanners. Certain suchembodiments identify and locate the coronary arteries and/or aortaautomatically, rather than by operator input.

In describing various embodiments, the terminology used herein is notintended to be interpreted in any limited or restrictive manner, simplybecause it is being utilized in conjunction with a detailed descriptionof certain exemplary embodiments. Furthermore, embodiments may includeseveral novel features, no single one of which is essential topracticing embodiments described herein.

Many embodiments described herein are useful in computer-implementedanalysis processes of CT images. In these processes, CT imaging data areanalyzed using software code running on general purpose computers, whichcan take a wide variety of forms, including, but not limited to, networkservers, workstations, personal computers, mainframe computers, and thelike. The code which configures the computer to perform these analysesis typically provided to the user on a computer-readable medium, such asa CD-ROM. The code may also be downloaded by a user from a networkserver which is part of a local or wide-area network, such as theInternet.

The general purpose computer running the software will typically includeone or more input devices such as a mouse and/or keyboard, a display,and computer-readable memory media such as random access memoryintegrated circuits and a hard disk drive. One or more portions of thecode or all of the code may be remote from the user and, for example,resident on a network resource such as a LAN server, Internet server,network storage device, etc. In typical embodiments, the softwarereceives as an input a variety of information, such as the CT imagingdata and any user-determined parameters for the analysis.

Embodiments are described herein using flow diagrams that have steps ina particular order, and the order of the steps in the flow diagrams isnot to be considered to be limiting. Other methods with different ordersof steps are also compatible with embodiments described herein. Inaddition, other methods with additional steps are also compatible withembodiments described herein.

FIG. 1 is a flow diagram of an exemplary embodiment of a method 10 formeasuring calcification in at least a portion of the vascular system ofa living body. The method 10 comprising providing at least one x-raycomputed tomography (CT) image comprising voxels indicative of x-rayattenuation of corresponding body structures in an operational block 20.The method 10 further comprises automatically identifying calcifiedregions within the CT image in an operational block 30. The calcifiedregions have x-ray attenuation values above a predetermined thresholdvalue. The method 10 further comprises automatically determining acalcium content corresponding to the identified calcified regions in anoperational block 40.

FIG. 2 is a flow diagram of an exemplary embodiment of a method 100 formeasuring calcification in at least one coronary artery of a livingbody. The embodiment of the method 100 illustrated by FIG. 2 comprisesproviding at least one CT image comprising voxels indicative of x-rayattenuation of corresponding body structures in an operational block110. The method 100 further comprises automatically identifying anapproximate location of the coronary artery within the CT image in anoperational block 120. The method 100 further comprises automaticallydefining a region-of-interest (ROI) which surrounds the approximatelocation of the coronary artery in an operational block 130. The method100 further comprises automatically identifying calcified regions withinthe ROI in an operational block 140. The calcified regions have x-rayattenuation values above a predetermined threshold value. The method 100further comprises automatically determining a calcium contentcorresponding to the sum of the x-ray attenuation values of theidentified calcified regions in an operational block 150.

In the operational block 110, at least one CT image is provided, the CTimage comprises voxels indicative of x-ray attenuation of correspondingbody structures. In certain such embodiments, a spiral or helical x-raycomputed tomography (CT) scanner is used to generate a series ofcontiguous, two-dimensional axial CT images of cross-sectional views ofthe patient's internal organs. Each axial CT image comprises a pluralityof voxels wherein the intensity of each voxel is representative of anx-ray attenuation value of a corresponding location within the body.Calcium has a higher density or x-ray attenuation value than does normalbody tissue, so the axial CT images provide contrast betweencalcification and surrounding tissue. As used herein, voxels within theimages which contain calcifications are referred to by the term“calcified regions.”

In the operational block 120, the approximate location of a coronaryartery is automatically identified within the CT image. As used herein,the term “automatically identified” denotes that the operation ofidentifying details in the image by the computer system, in hardware,software, or both, rather than by the operator. FIG. 3 is a flow diagramof one embodiment of the operational block 120 in which each CT image isan axial CT slice of a three-dimensional x-ray CT image. The flowdiagram of FIG. 3 refers to an embodiment in which the approximatelocations of all four of the coronary arteries are automaticallyidentified. Other procedures for approximately locating one or morecoronary arteries are compatible with embodiments described herein,including procedures which permit operator intervention to correct forspurious results.

In the operational block 121, the aorta is found on a first axial CTslice (e.g., the axial CT slice which is at the top or the most superiorposition). In certain such embodiments, finding the aorta comprisesidentifying an aortic cross-section having a center in the first axialCT slice. The aortic cross section of certain embodiments is defined tobe a sharp-edged, generally-circular object having a diameter betweenapproximately 1.5 centimeters and approximately 3.0 centimeters, andhaving an x-ray attenuation value between approximately +20 HU and +40HU. Furthermore, the aorta is typically bounded to the anterior andright side by the lungs, which have an x-ray attenuation value ofapproximately −800 HU.

In other embodiments, the aortic cross section is found on the firstaxial CT slice by cross-correlating the first axial CT slice with amodel image. Because the ascending aorta is a cylindrical tubeapproximately parallel to the axis of the body, it appears as a circlein axial tomographic images. It is filled with blood with a density ofslightly more than 1 g/ml and is surrounded by less dense fat and lungtissues. Thus, it is an easily identifiable object. The embodiment of atypical aorta identification method employs cross-correlation with amodel image depicting a bright uniform 2-3 cm circle in a less densebackground. This embodiment differs from standard cross correlationtechniques by incorporating dynamic scaling to accommodate a range ofaortic diameters.

In certain embodiments, the aortic cross-section of the first axial CTslice defines a coordinate system for the subsequent stages of themethod 100. For example, the center of the aortic cross section canserve as a coordinate system origin. In addition, the locations of thecoronary arteries can be determined to be a predetermined distance fromthis coordinate system origin. For example, the right coronary artery incertain embodiments is found to be one aortic diameter away from a planethat passes through the center of the aortic cross section at 45 degreesto the patient's spine.

In the operational block 122, the aortic valve is found by following theaorta down through subsequent lower axial CT slices in the inferiordirection. In certain embodiments, finding the aortic valve comprisesrepeating the process of cross-correlation of the model image withsubsequent axial CT slices in the inferior direction. Each axial CTslice will exhibit a corresponding cross section of the aorta as acircular body, as described above, shifted slightly from the position ofthe circular body in the previous axial CT slice. This process continuesfor subsequent axial CT slices in the inferior direction until an axialCT slice is found which does not exhibit a circular body correspondingto the aorta. This axial CT slice corresponds to the aortic valve at theinterface between the aorta and the heart.

The heart has four large chambers: two atria and two ventricles. Theventricles and atria are separated by a planar structure. The aorta andpulmonary arteries are oriented such that they meet the ventricles atthis plane as well. Thus, all four heart valves lie in approximately thesame plane. The approximate location of this planar structure is readilyidentified on images from gross cardiac anatomy and by following theaorta to its terminus. As used herein, the term “valve plane” refers tothis common plane. In the operational block 123, a first slabsurrounding the valve plane is selected. The first slab can be selectedby defining a generally planar region in the three-dimensional CT imagewhich includes the aortic valve and is oriented at approximately 45degrees from the axial direction. In certain embodiments, the thicknessof the first slab is between approximately 1 centimeter andapproximately 3 centimeters.

Certain embodiments exploit the fact that a large fraction of the courseof coronary arteries along the surface of the heart coincides with thevalve plane. The right coronary artery emerges from the right anteriorside of the aorta just above the aortic valve and follows the valveplane right anterior margin of the valve plane. The left main coronaryartery emerges from the left side of the aortic valve and quicklybranches into the circumflex coronary artery which follows the posteriorleft margin of the valve plane.

The coronary arteries are positioned on the outer surface of the heartand each artery is generally surrounded by a region of fat tissue. Inthe operational block 124, the first slab is convolved with a firsthighlighting function to highlight a first set of voxels having bloodx-ray attenuation values against a background of voxels having fat x-rayattenuation values. These “blood x-ray attenuation voxels” correspond tothe coronary arteries. In certain embodiments, the first highlightingfunction has a bright spot surrounded by a dark ring. Varioushighlighting functions are compatible with embodiments described herein,including, but not limited to, the sinc function (i.e.,sinc(x)=sin(x)/x). The Fourier transform of the sinc function is arectangular function, thereby making the convolution of the sincfunction with the first slab relatively easy to calculate. In theoperational block 125, the first set of blood x-ray attenuation voxelsare connected together to form traces of the right, left main, and leftcircumflex coronary arteries.

In the operational block 126, a second plane containing the aorticvalve, the inferior-most point of the right coronary artery, and theapex of the heart is defined. The positions of the aortic valve and theinferior-most point of the right coronary artery are determined asdescribed above. The position of the apex of the heart is determined bynoting that the heart is positioned within a surrounding region of fattissue and lung tissue. By searching generally perpendicularly to thevalve plane (i.e., in the left anterior direction), the furthest-mosttransition from blood x-ray attenuation values to fat or lung x-rayattenuation values is found. This transition point is used as the apexof the heart to define the second plane.

In the operational block 127, a second generally planar slab surroundingthis second plane is selected. In certain embodiments, the second slabhas a thickness between approximately 1 centimeter and approximately 3centimeters. In the operational block 128, the second slab is convolvedwith a second highlighting function to highlight a second set of bloodx-ray attenuation voxels. In certain embodiments, the secondhighlighting function is the same as the first highlighting function,which in certain such embodiments is the sinc function. In theoperational block 129, the second set of blood x-ray attenuation voxelsare connected together to form a trace of the left anterior descendingartery.

In the operational block 130, a region-of-interest (ROI) isautomatically defined. The ROI surrounds the approximate location of thecoronary artery. As used herein, the term “automatically defined”denotes that the operation of defining the ROI is performed primarily bythe computer system in hardware, in software, or in both hardware andsoftware, rather than by the operator. Various procedures forautomatically defining the ROI are compatible with embodiments describedherein, including procedures which permit operator intervention tocorrect for spurious results.

In certain embodiments, the ROI is defined to be a generally cylindricalvolume surrounding the approximate location of the coronary artery. Incertain such embodiments, the axis of the generally cylindrical ROIfollows the trace of the coronary artery as determined herein. Theradius of the generally cylindrical volume is large enough to encompassthe entire coronary artery, and in certain embodiments, the radius isless than or equal to approximately one centimeter.

In the operational block 140, calcified regions are automaticallyidentified within the ROI. The calcified regions have x-ray attenuationvalues above a predetermined threshold value. As used herein, the term“automatically identified” denotes that the operation of identifying thecalcified regions is performed primarily by the computer system inhardware, in software, or in both hardware and software, rather than bythe operator. Other procedures are compatible with embodiments describedherein, including procedures which permit operator intervention tocorrect for spurious results. Exemplary procedures for automaticallyidentifying calcified regions are described by A. S. Agatston et al., inCoronary Calcification: Detection by Ultrafast Computed Tomography, W.Stanford and J. A. Rumberger (eds.), Ultrafast Computed Tomography:Principles and Practice, Mount Kisco, N.Y., Futura Publishing Co., Inc.,pp. 77-95, which is incorporated in its entirety by reference herein.

In certain embodiments, the calcified regions are automaticallyidentified using a pair of criteria corresponding to (i) x-rayattenuation and (ii) size. Using the first criterion of x-rayattenuation, the voxels having x-ray attenuation values greater than orequal to a predetermined threshold value are identified. In certainembodiments using CT images, to qualify as a calcified region, the x-rayattenuation value of the voxel is greater than or equal to approximately130 HU. In other embodiments, the predetermined threshold value is twostandard deviations greater than the mean x-ray attenuation value of theCT image of the heart region. Other predetermined threshold values forthe x-ray attenuation criterion are compatible with embodimentsdescribed herein.

Using the second criterion of size, the volume of the voxels neighboringone another and satisfying the x-ray attenuation criterion iscalculated. In certain embodiments, a group of neighboring voxels havinga total volume greater than or equal to a predetermined volume isinterpreted as corresponding to calcified regions. For example, thepredetermined volume of certain embodiments is two or more imageelements, while in other embodiments, the predetermined volume is fourimage elements. This size criterion helps to avoid including randomisolated noise in the calcium content determination. Other predeterminedvalues for the size criterion are compatible with embodiments describedherein.

Calibration phantoms can provide a method to precisely quantify thex-ray attenuation by overcoming technical variations in CT scanners andphysical differences from patient to patient. For example, QCT bonedensitometry measurements of the lumbar spine have been improved byscanning phantoms simultaneously with the patient to calibrate the imageto a known standard. Exemplary calibration processes are described byArnold in U.S. Pat. No. 4,922,915 and in U.S. Pat. No. 6,990,222, bothof which are incorporated in their entirety by reference herein.

In the operational block 150, a calcium content corresponding to the sumof the x-ray attenuation values of the identified calcified regions isautomatically determined. As used herein, the term “automaticallydetermined” denotes that the operation of determining the calciumcontent is performed primarily by the computer system in hardware, insoftware, or in both hardware and software, rather than by the operator.Other procedures are compatible with embodiments described herein,including, for example, procedures which permit operator intervention tocorrect for spurious results.

In certain embodiments, the x-ray attenuation values of all thecalcified regions are calibrated and summed together to provide ameasure of the calcium content expressed in mass units referred to thecalibration phantom. This procedure can provide a good approximation ofthe total mass of the calcified regions. Other weighting procedures orsummation procedures are compatible with embodiments described herein.In addition, as described herein, by calibrating the images, the voxelx-ray attenuation values can be converted to mass units.

In certain embodiments, the method 100 further comprises segmenting outportions from the three-dimensional CT image that do not correspond tothe heart. Such a process identifies and omits from further analysisdata regions that are not of interest. The process can simplify and/orspeed up the subsequent analysis. While certain embodiments perform thesegmentation prior to the operational block 120 of FIGS. 2 and 3, otherembodiments perform the segmentation at other stages of the method 100.

FIG. 4 is a flow diagram of one embodiment of an operational block 160for segmenting out portions from an x-ray CT image that do notcorrespond to the heart. The operational block 160, as illustrated byFIG. 4, provides a rough segmentation of the heart from the rest of thethree-dimensional x-ray CT image. Other segmentation processes arecompatible with embodiments described herein.

In certain embodiments, the operational block 160 comprises identifyinga first axial CT slice corresponding to an inferior boundary of theheart in an operational block 161. In certain such embodiments, thefirst axial CT slice is identified automatically by the analysis system.A plot of the cross-sectional density as a function of the axialdirection is produced by summing the x-ray attenuation values for thevoxels in each axial CT slice. Because the x-ray attenuation valuescorresponding to lung tissue are lower than those corresponding toblood, fat, or muscle tissue, the largest change in the cross-sectionaldensity between subsequent axial CT slices is deemed to denote thediaphragm (i.e., the boundary between the lungs and the liver). Thediaphragm represents a uniformly present landmark which is positionednext to and in the inferior direction from the heart, so the first axialCT slice is typically the slice which denotes the diaphragm. In certainother embodiments, the first axial CT slice is identified by theoperator, who provides operator input to the analysis system. Otherprocedures for identifying the first axial CT slice corresponding to theinferior boundary of the heart are compatible with embodiments describedherein.

In certain embodiments, the operational block 160 further comprisesidentifying a cross section of the heart within a second axial CT slicein an operational block 162. The second axial CT slice is spaced adistance in the superior direction from the first axial CT slice. Incertain embodiments, the distance is predetermined to be approximatelyone centimeter, and the cross section of the heart is identified to bethe largest round object within the second axial CT slice. In otherembodiments, each of the axial CT slices superior to the first axial CTslice is examined, and the largest round object within each of the axialCT slices is determined. The cross section of the heart is thenidentified to be the largest of these round objects from the axial CTslices. The cross section of the heart typically has a diameter ofapproximately 10 centimeters. Other procedures for identifying the crosssection of the heart are compatible with embodiments described herein.

In certain embodiments, the operational block 160 further comprisesdefining an image cylinder in an operational block 163. The imagecylinder has a cylinder diameter equal to a diameter of the heart crosssection and has a cylinder axis at a predetermined orientation withrespect to the axial direction. In certain embodiments, the cylinderaxis of the image cylinder is selected to coincide with the axis of theheart so that the heart is within the volume defined by the imagecylinder. The cylinder axis in certain embodiments is defined bystarting at the center of the heart cross section in the second axial CTslice and in subsequent inferior slices, shifting the center anteriorlyand to the left by a predetermined amount. Similarly, in subsequentsuperior slices from the second axial CT slice, the center is shiftedposteriorly and to the right. The cylinder axis, defined to be the linedefined by the center, is thus slanted to correspond to the axis of theheart. In certain other embodiments, the size, position, and orientationof the image cylinder can be determined in part by operator input. Otherprocedures for defining the image cylinder are compatible withembodiments described herein.

In certain embodiments, the operational block 160 further comprisesomitting voxels from the image in an operational block 164. The omittedvoxels are outside the volume defined by the image cylinder. In certainembodiments, omitting the voxels from the image comprises omitting thevoxels from subsequent analysis steps, as opposed to removal of voxelsfrom the image. In certain other embodiments, the image is modified toremove the omitted voxels.

In certain embodiments, the operational block 160 further comprisesomitting voxels corresponding to bone within the volume defined by theimage cylinder in the operational block 165. In certain suchembodiments, the voxels corresponding to bone are identified to includevoxels on the surface of the image cylinder having x-ray attenuationvalues above a threshold and voxels within the image cylinder connectedto these surface voxels and having x-ray attenuation values above thethreshold. In certain embodiments, the threshold is predetermined to beapproximately 130 HU, while in other embodiments, the threshold is setto be larger than a mean x-ray attenuation value of the image cylindervolume. In certain embodiments, omitting the voxels corresponding tobone comprises omitting the voxels from subsequent analysis steps, asopposed to removal of voxels from the image. In certain otherembodiments, the image is modified to remove the omitted voxels.

In certain embodiments, the method 100 further comprises calibrating theCT image. Such a process calibrates the image data using a calibrationstandard, enabling quantitative density measurements using the imagedata. While certain embodiments perform the calibration prior to theoperational block 120 of FIGS. 2 and 3, other embodiments perform thecalibration at other stages of the method 100.

In certain embodiments, measurements from a calcium-equivalent phantomare used to derive a calibration equation to calibrate the CT image. Inother embodiments, measurements from an in vivo blood sample are used toderive the calibration equation. Such embodiments of calibrating the CTimage are described, for example, in U.S. Pat. No. 6,990,222, referencedabove.

In certain embodiments, calibrating the CT image comprises generating ahistogram of the x-ray attenuation values of the voxels of the CT image.Typically, such a histogram will exhibit one or more peaks correspondingto x-ray attenuation values for blood and muscle, fat, bones, and lungs.An exemplary histogram is illustrated in FIG. 5 for a CT image. One ormore of these peaks can be then be used to calibrate the image. Forexample, a histogram of the x-ray attenuation distribution may exhibit agenerally Gaussian peak due to blood and muscle tissue having a meanvalue between approximately +20 and +40 HU and a generally Gaussian peakdue to fat tissue having a mean value between approximately −120 and−180 HU. A linear calibration equation can be calculated to shift themean values of the two Gaussian peaks to +20 HU and −100 HU,respectively, and this calibration equation can be applied to the voxelsof the CT image.

In certain embodiments, the method 100 further comprises removing streakartifacts from the CT image. While certain embodiments perform theremoval of streak artifacts prior to the operational block 120 of FIGS.2 and 3, other embodiments perform the removal at other stages of themethod 100. In certain embodiments, removing streak artifacts comprisesperforming adaptive histogram equalization on the image, while otherembodiments comprise performing non-adaptive histogram equalization onthe image. Adaptive histogram equalization is performed by breaking theimage into small, overlapping regions and applying histogramequalization to each region separately. The non-adaptive form ofhistogram equalization has been described by R. C. Gonzalez and P. Wintzin Digital Image Processing, 1977, Addison-Wesley Publishing Company,Reading, Massachusetts, which is incorporated in its entirety byreference herein. Other image restoration procedures for removing streakartifacts or for otherwise smoothing out noise from the image arecompatible with embodiments described herein.

FIG. 6A schematically illustrates a surface rendered three-dimensionalCT image of the heart showing the location of the right coronary artery171, the left anterior descending artery 172, and the main coronaryartery 173. These locations are determined in three-dimensional spacewith position coordinates representing the approximate locations of thecoronary arteries. FIG. 6B schematically illustrates sagittal andcoronal reformations of the data illustrated in FIG. 6A.

FIG. 7, comprising into FIGS. 7A, 7B, and 7C, is a flow diagram ofanother embodiment of a method for measuring coronary arterycalcification of a living body. As shown in FIG. 7, N CT axial slices ofthe heart and chest are formed in a step 200, and calibration of the CTslices is performed in a step 201 using the calibration equation. Theentire heart volume is segmented out in three-dimensional space usingcalibrated pixels to define edges in step 202. The aorta is located bysize, shape, and density criteria in a step 203. The softwareautomatically searches in the inferior direction to find the aorta andheart muscle interface in a step 204. Segmentation methods well known inthe field are used to segment off the aorta in a step 205. A histogramanalysis is next performed on the total heart volume in a step 206. Itis useful to apply corrections and smoothing to CT slices of the heartregion in a step 207. The smoothing routine creates a new image withmean pixel densities equal to the histogram mode of the entire heart.

The software begins coronary artery location search from the aorta/heartwall interface point in a step 208. The minimum aortic diameter andlocation of a most complete circular shape is used as a startinglocation. The first CT slice superior to the heart is displayed. Theboundaries of the heart have previously been identified. Then, in a step209, the boundaries of the first corrected axial slice are searched. Ifboundary contours have an inflection point, this point is recorded inx,y,z coordinates in a step 210. If the boundary has a region with oneor more pixels of fat density, this region is recorded with x,y,zcoordinates. If the boundary has a calcium density pixel (2 or morestandard deviations greater than heart histogram mode), this region isrecorded with x,y,z coordinates. The borders are preferably defined witha width of about 5 pixels.

In a step 213, the procedure of steps 209 to 212 is repeated for thesecond corrected axial slice inferior to the first slice. In a step 214,the procedure of steps 209 to 212 is repeated for all slices up to andincluding the Nth corrected axial slice inferior to the first slice. Ina step 215, the detected regions are ranked for score based on calciumdensity being equal to 3, fat density being equal to 2, and inflectionpoint being equal to 1. In a step 216, the weighted location scores areaveraged to determine a line in three-dimensional space connectingpoints on all slices.

In a step 217, the operator may delete points as a manual override ofassumed artery positions at any time. The artery locations in an x,y,zcoordinate system are determined. The line identifying the locations isanalyzed onto a three-dimensional surface rendered image in a step 218.As described above, FIG. 6A shows the surface rendered three-dimensionalimage of the heart with the location of the main coronary artery 173,the right coronary artery 171, and the left anterior descending artery172. These locations are determined in three-dimensional space withx,y,z coordinates and represent the appropriate locations of thearteries. FIG. 6B shows sagittal and coronal reformations of the samedata as FIG. 6A with the locations of three of the coronary arteriesalso shown on these views. The software automatically places athree-dimensional search ROI around the measured artery positions instep 219. The diameter of the three-dimensional ROI is small relative tothe heart and is on the order of 5 or more pixels in diameter.

If, in a step 220, any pixels in the ROI are above the definedthreshold, they are included. The location of the calcium is recordedwith three-dimensional coordinates of each pixel in a step 221. Pixelswhich are adjacent on the slice or on adjacent slices will be consideredin a step 222. The sum of voxels and the voxel volume times density foreach coronary artery are summed on all images in a step 224, and theresults are stored and printed as the final calcium measures. Thecalcium measure may be a calibrated calcium score or may be calciummass.

FIG. 8 is a flow diagram of one embodiment of a method 300 for measuringcoronary artery calcification of a living body. In an operational block310, the method 300 comprises providing a plurality of CT images, andeach CT image comprises voxels indicative of x-ray attenuation ofcorresponding body structures.

In an operational block 320, the method 300 further comprisesidentifying boundaries of the heart within the plurality of CT images,with the boundaries defining a heart volume. Several hundred HU separateblood and lung x-ray attenuations. Almost any threshold technique can beused to identify the boundaries of the heart within the images.

In an operational block 330, the method 300 further comprises dividingthe heart volume into an inferior region, a middle region, and asuperior region. From the end of the aorta, divide the heart into fourquadrants. The right coronary artery is in a right anterior quadrant,the left anterior descending coronary artery is in a left anteriorquadrant, the circumflex coronary artery is in a left posteriorquadrant, and the fourth (right posterior) quadrant is empty of coronaryarteries. Near the aorta (e.g., within 3 cm to the left of the aorta) isthe left main coronary artery.

In an operational block 340, the method 300 further comprises locatingcalcium within the inferior region and attributing the inferior regioncalcium to calcification of the coronary arteries. Voxels correspondingto calcium can be identified as described above.

In an operational block 350, the method 300 further comprises locatingcalcium within the middle region, attributing a portion of the middleregion calcium to calcification of the coronary arteries and attributinga remaining portion of the middle region calcium to calcification of theheart valves. In certain embodiments, the portion of the middle regioncalcium attributed to calcification of the coronary arteries comprisescalcium within the middle region which is within a shell volume at anouter edge of the heart volume. In such embodiments, the shell volumehas a predetermined thickness. The coronary arteries are typicallywithin 2 cm of the surface of the heart.

In an operational block 360, the method 300 further comprises locatingcalcium within the superior region and attributing a portion of thesuperior region calcium to calcification of the coronary arteries and aremaining portion of the superior region calcium to calcification of theaorta. The aortic rim calcium is typically on the boundary of the aorticimage which was followed as described above.

The methods described herein can be used to automatically locate otherarteries in the body and to measure calcium. For example, a method toautomatically locate and measure calcium in the abdominal aorta is shownin FIG. 9. Since the aorta does not move greatly, single slice and lowerspeed CT scanners can be used to acquire the images. From a completeexam of several CT slices, the method of this embodiment can beunderstood with reference to FIGS. 5, 9, 10, 11, and 14.

FIG. 10 shows a depiction of a CT slice through the abdomen of apatient. The calibration reference phantom 410 contains three samples ofvarying calcium density. Other phantoms can be used in separate orsimultaneous calibration. The trabecular region of one vertebral body412 is surrounded by the dense cortical shell 411. The usual location ofthe abdominal aorta 413 is directly anterior to the vertebral body.Aortic calcification 414 is depicted.

In FIG. 9, comprising FIGS. 9A and 9B, the software first automaticallylocates the calibration phantom and places ROIs in the phantom sample ina step 500. The phantom calibration equation is determined by regressionanalysis in a step 501. The slope and intercept of this regressionequation may or may not later be combined with the in vivo blood samplecalibration to obtain a hybrid calibration equation. The regressionintercept of the calibration phantom will be re-calibrated by shiftingits value based on the blood pool calibrated pixel values such that allCT slices will produce the same calibrated blood CT values. This is animprovement of the phantom calibration method to allow a second-ordercorrection determined from a homogeneous tissue within the body of aknown density (blood), which will further improve accuracy andprecision. Calibration is next applied to each CT image or to a croppedsection of that image 502. The location of a vertebral body is nextfound automatically. The exterior contours of the vertebral body arenext identified in a step 503. This can be accomplished with relativelysimple edge detection algorithms since the tissue density contrast islarger between the cortical bone and the surrounding soft tissue. Thethree-dimensional coordinates of the exterior cortical bone contours arerecorded in a step 504. The algorithm then creates a series of spatialshifts one pixel width and performs a search along that shifted contour.

In FIG. 11, the search procedure is shown in more detail. The firstcontour N=1 is directly adjacent and follows the vertebral contour 421.Pixels located on this contour above the threshold are identified andlocated. The contour is next shifted one pixel to N=2, and the search isrepeated on this contour in a step 505. This procedure is repeated Ntimes to a final contour 420. This creates a search region sufficientlylarge to insure the aorta will be included in a step 506. In a step 507,the maximum contour may be sized proportionately to the cross-sectionalarea of the image to account for differences in patient sizes. Since theaorta is positioned anterior to the vertebral body and is surrounded bysoft tissue in a relatively large body cavity, ribs and bones or otherdistracting structures are not present to confuse the search algorithm.

Using shape and density constraints, the aorta images can usually belocated automatically. The use of the hybrid calibration aids the searchby defining in quantitative and reproducible units the edge of theaorta. The final region of search calcifications is located by thresholdanalysis. The threshold is preferably calibrated to the calibrationequation.

When pixels are located that contain calcium, as defined above thethreshold value, the pixels are tested to see if neighboring pixels bothin that image and in adjacent images in the stack contain calcium instep 508. In some cases, a calcification will be arbitrarily defined ashaving three or more pixels before it is scored as calcium. Singlepixels, unless of very high density, may be noise and are not scored.The pixels which meet these detection criteria are highlighted in a step509 and may be displayed on a reformatted or axial image for operatorreview. The volume and the mass of a calcification may be calculated ina step 510. A further calibration using the hybrid calibration equationmay be applied here in a step 511 or the calibration may have alreadybeen applied earlier in the procedure. As described above, FIG. 5 showsa histogram of the voxels in a ROI containing blood and heart tissue. Asimilar histogram peak, although with greater noise, will be obtainedfrom a ROI of the abdominal aorta.

In a step 512, the search and quantification step is repeated N times toanalyze all CT slices. The results for each slice are summed andpresented as a total calcium score or total calcium mass in step 513.

In another method to automatically locate and quantify aortic calcium, adifferent search algorithm is used as illustrated in FIG. 12.

In FIG. 12, the calibration phantom and vertebral body are automaticallylocated by known methods. An elliptical ROI is located automaticallywith the trabecular region of the vertebral body in a step 602 in FIG.13, comprising FIGS. 13A and 13B. FIG. 12 shows a depiction of avertebral body and aorta showing the search lines of the second searchalgorithm. The trabecular region 412 of the bone is indicated. Anelliptical ROI is located within the trabecular region and the center ofthe ROI forms the initial point for a fan search pattern 416. The searchpattern has a 180° angle and extends a distance X from the vertebralcontour 415, which insures the aorta 413 is included in the final searchregion. Once the search region is defined 416, the software searches theregion by threshold analysis to locate the aorta and aorticcalcification as discussed above and as shown in FIG. 9.

Another embodiment of the methods to automatically detect and quantifyaortic calcifications can be understood from FIG. 13 in connection withFIG. 14.

Images from N CT slices of the body containing a larger artery may becalibrated using a reference calibration phantom. Alternatively, withless accuracy, the algorithm will operate without calibration. Theartery may be the abdominal aorta. The algorithm finds the vertebralbody and anterior cortical bone contour in a step 600. The calibrationmay be applied in a step 601. In a step 602, the vertebral region withan auto ROI can be used as a beginning point to position a locatorsearch region.

Although FIG. 12 shows a fan beam search region, preferably this regionwill be extended and cropped to form a rectangular locator ROI whichincludes the aorta. The locator ROI may or may not include the vertebralbody or a portion of the vertebral body.

When the locator search region is automatically located on all axialimages, these regions are reformatted into preferably sagittal images.If no part of the vertebral body is included, the coronal reformationmay also be used.

The sagittal view is displayed in a step 604 so the operator can verifythat the aorta is included. The axial images have been cropped toexclude most of the body, leaving only a relatively thin section oftissue defined by the locator regions. This provides high contrast inthe sagittal image to allow visualization of the aorta andcalcifications. The operator can next place a search point to define theinferior and superior extent of the aorta in a step 605. Alternatively,the software may use shape and density constraints to locate the aortaautomatically in a step 606. The software next connects a point A and apoint B and displays the line AB overlaid on the sagittal image. Theoperator verifies the location of the line and verifies that the lineintersects the aorta on all slices in a step 607. The software uses theintersection of the line AB and each axial slice at the intersection todefine a search seed point that lies within the aorta in a step 608.Region growing techniques are used to define the aorta and its boundaryin a step 609. Calibrated edges are beneficial to improvereproducibility on repeat scans. Sanity checks are performed on theresults by comparing expected shapes, diameters and density ranges tothose measured on the aorta in a step 610.

In a modification to this technique, the abdominal aorta is locatedsemi-automatically from axial CT images and sagittal reformatted images.The operator applies cursor marks “within” the aortic image on CT sliceswith a search point. Preferably the first superior image is marked bycursor point within the aortic image, then marked again after thebifurcation on the aorta with two more search points.

Reconstructed sagittal and coronal views of a subset of pixels aredefined by the locator box using all CT slices. Contrast enhances theresulting image to display arterial outer margins. A search point isplaced by manual cursor at both the inferior (A) and superior (B) endsof the displayed aortic image. The point A is connected to the point Bwith visual verification that the connecting line transverses and stayswithin the aorta on all slices. Points along the line AB are used asbeginning search points in each axial image to define the aorticboundaries by region growing techniques.

Histogram analysis is performed on the aortic regions with Gaussiancurve fits in a step 611. A calibration correction may be applied topixels in the locator region using the histogram mode and the knownblood density in a step 612. Pixels above a set threshold are detectedin a step 613 and are summed to provide calcium area or calcium mass ina step 614. The sagittal reformed images may be displayed with thedetected calcifications shown on the view in a step 615. This aids inthe diagnostic report and provides a quick visual indication of calciumlocation in the aorta. The final total calcium measure is recorded anddisplayed in a final report in a step 616.

Although described above in connection with particular embodiments ofthe present invention, it should be understood the descriptions of theembodiments are illustrative of the invention and are not intended to belimiting. Various modifications and applications may occur to thoseskilled in the art without departing from the true spirit and scope ofthe invention as defined in the appended claims.

1. A method for segmenting the image of the heart using athree-dimensional (3-D) volume of images of a subject without theintroduction of contrast material into the subject, the imagescontaining imaging elements representative of physical properties of thebody of the subject, the method comprising: acquiring a 3-D volume setof images containing voxels having representative properties of the bodytissues; analyzing the set of images in a computer to identify voxelshaving intensities likely to represent blood and muscle; analyzing theidentified voxels to determine which voxels satisfy preset criteria;rejecting voxels as not heart tissue if the preset criteria are notsatisfied; and determining that the remaining voxels constitute hearttissue.
 2. The method of claim 1, wherein the preset criteria includevoxel connectivity definitions.
 3. The methods of claim 1, wherein thepreset criteria includes a minimum volume of voxels of heart tissue. 4.The methods of claim 1, wherein the preset criteria include a definedfraction of voxels represented in a distribution of the intensities ofthe voxels contained within the heart tissue volume.
 5. The method ofclaim 1, wherein the 3-D volume of images are MRI images.
 6. The methodof claim 1, wherein the definition of ‘likely to represent blood andmuscle’ includes measures of voxel intensities, voxel homogeneitycriteria and location criteria.
 7. A non-invasive method for analyzingcoronary arteries of a living subject without iodine contrast materialbeing injected into the subject, the method comprising: providing aplurality of internal images of the heart, each image comprising imageelements indicative of properties of corresponding heart structures;identifying boundaries of the heart within the plurality of heartimages, the boundaries defining a heart volume; defining surfacecoordinates of the heart volume; defining a shell volume at the surfaceof the heart volume, the shell volume having a thickness greater thanthe diameter of coronary arteries; identifying the coronary arterieswithin the shell volume by using pixel intensity values and expectedshape values of the coronary arteries; and displaying the images of thecoronary arteries.
 8. The method as defined in claim 7, wherein thearteries are located in a coordinate system referenced to the surfacecoordinates of the segmented heart volume.
 9. The method as defined inclaim 7, wherein the artery images are displayed in a three-dimensionalimage referenced to three-dimensional locations of the plurality ofsegmented heart images.
 10. The method as defined in claim 7, whereinthe displaying of images of the coronary arteries is displayed on a 3-Dtranslucent or partially translucent image representation of the heartvolume.
 11. A system for detecting and quantifying abnormalities incoronary arteries of the body using x-ray computed tomography (CT), thesystem comprising: means for CT acquiring images of the arteries; meansfor determining locations of at least one coronary artery; means forautomatically positioning a three-dimensional search ROI which surroundsthe coronary artery location in at least one image; means for evaluatingimage elements within said three-dimensional search ROI to locate theartery boundaries; means for detecting the abnormalities in the coronaryarteries; and means for displaying the images of the artery withabnormalities.
 12. The system as defined in claim 11, wherein the imagesare calibrated using measurements from an external iodine-equivalentphantom by comparing images of the arteries with iodine contrast agentsand images of the iodine-equivalent phantom.
 13. The system as definedin claim 11, wherein the images are calibrated using measurements froman in vivo tissue sample.
 14. The system as defined in claim 11, whereinthe CT images are obtained following iodine injection into the subject.15. The system as defined in claim 11, wherein the artery boundarieshave been convolved with a function.
 16. The system as defined in claim11, wherein said abnormalities are soft plaque or calcified plaque. 17.The system as defined in claim 11, wherein said abnormalities arereduced diameters of the arteries.
 18. The method as defined in claim11, further comprising means for locating plaque within the arterywithout operator interaction.
 19. The method as defined in claim 11,where the artery is a coronary artery.
 20. A method to segment the aortausing a 3-D volume of images of a subject containing imaging elementsrepresentative of physical properties of the body of said subject,without the introduction of contrast material into said subject, themethod comprising: acquiring a 3-D volume set of images containingvoxels representative of the body tissues; analyzing the image set in acomputer to identify pixel density representations and shapes of imagestructures; and automatically identify the boundaries of the aorta bycomputer analysis of the identified pixel densities and structureshapes.
 21. The method as defined in claim 20, wherein the boundariesare expressed in a 3-dimensional coordinate system.
 22. The method asdefined in claim 20, wherein the image of the aorta is analyzed forblood density.
 23. The method as defined in claim 20, wherein the imagesare obtained using x-ray computed tomography (CT) and include;automatically placing a region-of-interest which includes the aorta;automatically removing bone image elements located within theregion-of-interest; automatically detecting outlines of arteriescontained within the region-of-interest; and automatically detectingabnormalities associated with the image of the aorta.
 24. Thesegmentation method of claim 23, wherein the bone image elements areremoved using connectivity calculations.
 25. The segmentation method ofclaim 23, wherein the detected outlines of the arteries are determinedusing the Hough Transform.
 26. The segmentation method of claim 23,wherein the arteries are detected using level-set algorithms.
 27. Amethod to segment an image of the heart of a subject using x-raycomputed tomography (CT) by acquiring a volumetric set of CT imagescontaining voxels representing x-ray attenuation of the subject, themethod comprising: automatically locating a 3-Dimensional (3-D)region-of-interest (ROI) volume which includes the image of the heart ofthe subject; automatically determining a three-dimensional coordinatesystem within the region of interest (ROI) volume; automaticallyanalyzing the three-dimensional ROI volume to identify voxels in thevolume which satisfy defined criteria; automatically identifying thevoxels which represent the cardiac blood pool and/or cardiac muscle ofthe subject; and automatically locating the surface coordinates of thesegmented heart volume to the coordinate system.
 28. The method of claim27, wherein the automatically defined and located 3-D ROI containing theheart is adjustable by operator interaction.
 29. The method of claim 27,wherein the segmentation is executable manually by an operator.